Predicting performance of content and electronic messages among a system of networked computing devices

ABSTRACT

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface, and, more specifically, to a computing and data storage platform that implements specialized logic to predict effectiveness of content in electronic messages as a function, for example, modifiable portions of the content. In some examples, a method may include receiving data signals to cause formation of an electronic message, determining a component of the electronic message, identifying one or more message performance criteria, characterizing the component to identify a component attribute, predicting the component attribute matches at least one of the message performance criteria, and transmitting the electronic message.

CROSS-REFERENCE TO APPLICATIONS

This application is a continuation application of copending U.S.Nonprovisional patent application Ser. No. 15/782,642, filed Oct. 12,2017 and entitled, “PREDICTING PERFORMANCE OF CONTENT AND ELECTRONICMESSAGES AMONG A SYSTEM OF NETWORKED COMPUTING DEVICES;” Thisapplication is related to U.S. patent application Ser. No. 15/782,635,filed on Oct. 12, 2017 and entitled, “COMPUTERIZED TOOLS TO ENHANCESPEED AND PROPAGATION OF CONTENT IN ELECTRONIC MESSAGES AMONG A SYSTEMOF NETWORKED COMPUTING DEVICES;” This application is also related toU.S. patent application Ser. No. 15/782,653, filed on Oct. 12, 2017 andentitled, “OPTIMIZING EFFECTIVENESS OF CONTENT IN ELECTRONIC MESSAGESAMONG A SYSTEM OF NETWORKED COMPUTING DEVICES;” all of which are hereinincorporated by reference in their entirety for all purposes.

FIELD

Various embodiments relate generally to data science and data analysis,computer software and systems, and control systems to provide a platformto facilitate implementation of an interface, and, more specifically, toa computing and data storage platform that implements specialized logicto predict effectiveness of content in electronic messages as afunction, for example, modifiable portions of the content.

BACKGROUND

Advances in computing hardware and software have fueled exponentialgrowth in delivery of vast amounts of information due to increasedimprovements in computational and networking technologies andinfrastructure. Also, advances in conventional data storage technologiesprovide an ability to store increasing amounts of generated data. Thus,improvements, in computing hardware, software, network services, andstorage have bolstered growth of Internet-based messaging applications,especially in an area of generating and sending information regardingavailability of products and services. Unfortunately, such technologicalimprovements have contributed to a deluge of information that is sovoluminous that any particular message may be drowned out in the sea ofinformation. Consequently, a number of conventional techniques have beenemployed to target certain recipients of the information so as tohopefully increase interest and readership of such information.

In accordance with some conventional techniques, creators of content andinformation, such as merchants and sellers of products or services, haveemployed various known techniques to target specific groups of peoplethat may be likely to respond or consume a particular set ofinformation. These known techniques, while functional, suffer a numberof other drawbacks.

The above-described advancements in computing hardware and software havegiven rise to a myriad of communication channels through whichinformation may be transmitted to the masses. For example, informationmay be transmitted via messages through email, text messages, websiteposts, social networking, and the like. As such, traditional approachesto communicate information have been generally focused on transmittinginformation coarsely, with attempts to focus transmission of informationto a certain number of possible consumers of interest. However,conventional approaches to leverage social media to reach particularaudiences (e.g., microsegments) have been suboptimal in securingparticipation in consuming information that, for example, will likelylead to a conversion (e.g., a product purchase). While functional, suchapproaches suffer a number of other drawbacks.

For example, various conventional approaches by which to identify aparticular recipient of information are generally vulnerable to lessprecise identification of, for example, a particular recipient'sengagement with such information. Consequently, traditional electronicmessage propagation techniques are typically less effective incommunicating to a broadest group of potentially interested consumers ofsuch information.

Thus, what is needed is a solution for facilitating techniques toenhance speed and distribution of content in electronic messages,without the limitations of conventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting computerized tools to generate anelectronic message targeted to a subset of recipients, according to someembodiments;

FIG. 2 depicts another example of an electronic message performancemanagement platform, according to various examples;

FIG. 3 is a diagram of an example of a user interface depicting adaptionof an electronic message during generation, according to someembodiments;

FIG. 4 is a diagram of an example of a user interface depicting adaptionof an electronic message during generation, according to someembodiments;

FIG. 5 is a diagram of an example of identifying performance metricsrelative to a geographic location for an electronic message duringgeneration, according to some embodiments;

FIG. 6 is a diagram of an example of identifying a level of complexityof components for an electronic message during generation, according tosome embodiments;

FIG. 7 is a diagram of an example of identifying subpopulation-dependentcomponents for an electronic message during generation, according tosome embodiments;

FIG. 8 is a flow diagram as an example of generating an adaptedelectronic message, according to some embodiments;

FIG. 9 is a diagram depicting an example of an electronic messageperformance management platform configured to harvest and analyzeelectronic messages, according to some examples;

FIG. 10 is a diagram depicting an example of a user interface configuredto accept data signals to identify and modify predicted performance of amessage component, according to some examples;

FIG. 11 is a diagram depicting an example of a user interface configuredto accept data signals to visually convey a predicted performance of amessage component, according to some examples;

FIG. 12 is a flow diagram as an example of predicting performancemetrics for an electronic message, according to some embodiments;

FIG. 13 is a diagram depicting an electronic message performancemanagement platform implementing a publishing optimizer, according tosome embodiments;

FIG. 14 is a flow diagram as an example of monitoring whetherperformance of an electronic message complies with predicted performancecriteria, according to some embodiments;

FIG. 15 is a diagram depicting an electronic message performancemanagement platform implementing a publishing optimizer configured topresent monitored performance values of a published electronic message,according to some embodiments; and

FIG. 16 illustrates examples of various computing platforms configuredto provide various functionalities to components of an electronicmessage performance management platform, according to variousembodiments.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting computerized tools to generate anelectronic message targeted to a subset of recipients, according to someembodiments. Diagram 100 depicts an example of a message generationsystem 150 configured to enhance speed and distribution of content inelectronic messages 172 as a function of, for example, a portion of thecontent, according to some embodiments. A portion of content in anelectronic message may include a symbol (e.g., a letter or number), aword, a group of words, such as a phrase, a message topic, or any othercharacteristic of the electronic message associated with, or descriptiveof, the message, according to various examples. In one example,electronic message performance management platform 160 may be configuredto analyze an electronic message being generated, and profferrecommended actions to enhance the speed and/or distribution ofelectronic messages 172 to users 108 a, 108 b, 108 c, and 108 d.

Diagram 100 depicts a message generation system 150 including a userinterface 120 and a computing device 130 (e.g., one or more servers,including one or more processors and/or memory devices), both of whichmay be configured to generate messages that may be configured for users108 a, 108 b, 108 c, and 108 d. Users 108 a and 108 b may interact viacomputing devices 109 a and 109 b with message network computing systems110 a and 110 b, respectively, whereas users 108 c and 108 d mayinteract via computing devices 109 c and 109 d with content sourcecomputing systems 113 a and 113 b, respectively. Any one or more ofmessage network computing systems 110 a and 110 b may be configured toreceive electronic messages, regardless of the context, for propagating(e.g., sharing), commenting, and consumption by any number of users forany reason, according to some examples. One or more of message networkcomputing systems 110 a and 110 b may be configured to distributeelectronic message content in any form in any digital media or channel.In various examples, message network computing systems 110 a and 110 bmay include any number of computing systems configured to propagateelectronic messaging, including, but not limited to, computing systemsincluding third party servers, such as third parties like Facebook™,Twitter™, LinkedIn™, Instagram™, Snapchat™, as well as other private orpublic social networks to provide social-media related informationaldata exchange services. In some examples, message generation system 150configured to enhance speed and distribution of content from any sourceof digital content. As such, users 108 a and 108 b may interact viacomputing devices 109 a and 109 b with message network computing systems110 a and 110 b, respectively, whereas users 108 c and 108 d mayinteract via computing devices 109 c and 109 d with content sourcecomputing systems 113 a and 113 b, respectively. Computing systems 113 aand 113 b may be configured to provide any type of digital content, suchas email, text messaging (e.g., via SMS messages), web pages, audio,video (e.g., YouTube™), etc.

According to some examples, message network computing systems 110 a and110 b may include applications or executable instructions configured toprincipally facilitate interactions (e.g., social interactions) amongstone or more persons, one or more subpopulations (e.g., private groups orpublic groups), or the public at-large. Examples of message networkcomputing systems 110 a and 110 b include the above-mentioned Facebook™,Twitter™, LinkedIn™, Instagram™, and Snapchat™, as well as YouTube™,Pinterest™, Tumblr™, WhatsApp™ messaging, or any other platformconfigured to promote sharing of content, such as videos, audio, orimages, as well as sharing ideas, thoughts, etc. in a socially-basedenvironment. According to some examples, content source computingsystems 113 a and 113 b may include applications or executableinstructions configured to principally promote an activity, such as asports television network, a profession sports team (e.g., a NationalBasketball Association, or NBA®, team), a news or media organization, aproduct producing or selling organization, and the like. Content sourcecomputing systems 113 a and 113 b may implement websites, email,chatbots, or any other digital communication channels, and may furtherimplement electronic accounts to convey information via message networkcomputing systems 110 a and 110 b.

In view of the structures and/or functionalities of message networkcomputing systems 110 a and 110 b and content source computing systems113 a and 113 b, an electronic message may include a “tweet” (e.g., amessage via a Twitter™ computing system), a “post” (e.g., a message viaa Facebook™ computing system), or any other type of social network-basedmessages, along with any related functionalities, such as forwarding amessage (e.g., “retweeting” via Twitter™), sharing a message,associating an endorsement of another message (e.g., “liking” a message,such as a tweet™, or sharing a Facebook™ post, etc.), and any otherinteraction that may cause increased rates of transmissions, or maycause increased multiplicity of initiating parallel transmissions (e.g.,via a “retweet” of a user having a relatively large number offollowers). According to various examples, an electronic message caninclude any type of digital messaging that can be transmitted over anydigital networks.

According to some embodiments, message generation system 150 may beconfigured to facilitate modification of an electronic message (e.g.,its contents) to enhance a speed and/or a rate of propagation at whichthe message may be conveyed in accordance with, for example, a value ofa performance metric. According to various examples, a value of aperformance metric may include data representing a value of anengagement metric, an impression metric, a link activation metric (e.g.,“a click-through”), a shared message indication metric, a followeraccount indication metric, etc., or any other like metric or performanceattribute that may be monitored and adjusted (e.g., indirectly bymodifying content) to conform transmission of an electronic message toone or more performance criteria.

Message generation system 150 is shown to include a computing device 120and display configured to generate a user interface, such as a messagegeneration interface 122. Message generation system 150 also includes aserver computing device 130, which may include hardware and software, ora combination thereof, configured to implement an electronic messageperformance management platform 160 (or “performance management platform160”), according to various examples. Performance management platform160 may include a message generator 162 configured to generateelectronic messages configured to urge or cause a targeted rate oftransmission and/or multiplicity of propagation (e.g. a rate of paralleltransmissions) for an electronic message responsive, for example, tointeractions with the message by recipient computing devices 109 a, 109b, 109 c, and 109 d (e.g., by recipient users 108 a, 108 b, 108 c, and108 d). Performance management platform 160 may also include aperformance metric adjuster 164 configured to adjust one or moreportions or components of an electronic message being generated atmessage generator 162 so that the generated electronic message mayachieve (or attempt to achieve) certain levels of performance asdefined, for example, by one or more performance metric criteria.

To illustrate a functionality of performance management platform 160,consider an example in which a user generates an electronic message 124via message generation interface 122 for transmission to one or moresimilar or different computing systems 110 a, 110 b, 113 a, and 113 b.As shown, a user may interact with computing device 120 to generate anelectronic message 124. Prior to transmission, performance managementplatform 160 includes logic configure to analyze and evaluate electronicmessage 124 to adjust one or more portions, such as portion 125, toenhance the rate of transmission, propagation, or any other performancemetric. In this example, the term “men” in an electronic message 124 isidentified by performance management platform 160 as having aperformance metric value of “0.250,” as shown in graphicalrepresentation 127. Optionally, a user may cause a selection device 126to hover over or select a graphical representation of portion 125. Inresponse, one or more message performance actions 123 may be presentedto the user. Here, at least one message performance action 123 includesa recommendation to replace the term “men,” having a performance metricvalue of “0.250,” with another term “persons” having a performancemetric value of “1.100” as shown in graphical representation 127. Notethat the magnitude of the performance metric value of “persons” isgreater than that for the term “men.” Thus, an electronic messageimplement the term “persons” may be predicted to perform better than ifthe term “men” was included.

In at least one example, the performance metric values of 0.250 and1.100 may represent a degree or amount of “engagement.” “Engagement” maybe described, at least in some non-limiting examples, as an amount ofinteraction with an electronic message. Data representing an engagementmetric may specify an amount of interaction with an electronic message.A value of an engagement metric value may be indicative of whether anelectronic message is accessed (e.g., opened or viewed), and whether anyone or more interactions with the electronic message are identified(e.g., generation of another electronic message responsive to an initialmessage). Hence, a user may desire to increase engagement by selectingto replace via user input 129 the term “men” with the term “persons.”With increased values of an engagement metric, the electronic messagemay be predicted to have greater amounts of interaction than otherwisemight be the case.

Other performance metrics and associated values may also be implementedto gauge whether electronic message 124 may achieve a user's objectives(e.g., a marketer or any other function), and to modify or adjustelectronic message 124 to meet a subset of performance criteria (e.g.,to meet an engagement of value “E” for a period of time “T”). Forexample, electronic message 124, as well as one or more componentsthereof, may be generated in accordance with another performance metric,such as an impression metric. An “impression” may be described, at leastin some non-limiting examples, as an instance in which an electronicmessage is presented to a recipient (e.g., regardless whether therecipient interacts with the message). A performance metric may includea “link activation,” which may be described, at least in somenon-limiting examples, as an instance in which a link (e.g., a hypertextlink) in an electronic message is activated. An example of a linkactivation is a “click-through,” among other message-related metrics orparameters with which to measure one or more levels of performance of anelectronic message, such as a Twitter post relating to a productpromotion and campaign. A performance metric may include a “shared”message, which may be described, at least in some non-limiting examples,as an instance in which a recipient 108 a, 108 b, 108 c, or 108 dre-transmits (e.g., “retweets”) an electronic message to one or moreother users, thereby propagating the message with multiplicity. Aperformance metric may include a “followed message” status, which may bedescribed as an instance in which recipients 108 a, 108 b, 108 c, or 108d may receive the electronic message based on a “following” relationshipto the original recipient. According to various embodiments, otherperformance metrics may be implemented in message generation system 150.

Diagram 100 further depicts performance management platform 160 beingcoupled to memory or any type of data storage, such as data repositories142, 144, and 146, among others. User account message data 142 may beconfigured to store any number of electronic messages 124 generated ortransmitted by performance management platform 160. For example,performance management platform 160 may be configured to storeelectronic message 124 (e.g., as historic archival data). Also,performance management platform 160 may be configured to determinecharacteristics or attributes of one or more components of an electronicmessage (e.g., as a published messages). According to some examples, acomponent of an electronic message may include a word, a phrase, atopic, or any message attribute, which can describe the component. Forexample, a message attribute may include metadata that describes, forexample, a language associated with the word, or any other descriptor,such as a synonym, a language, a reading level, a geographic location,and the like. Message attributes may also include values of one or moreperformance metrics (e.g., one or more values of engagement,impressions, etc.), whereby, at least in some cases, a value of aperformance metric may be a function of context during which anelectronic message is published (e.g., time of day, day of week, typesof events occurring locally, nationally, or internationally, thedemographics of recipients 108 a, 108 b, 108 c, and 108 d, etc.).Components of messages may be tagged or otherwise associated with any ofthe above-described metadata.

Further, performance management platform 160 may be configured toanalyze a subset of electronic message (e.g., including a quantity of 50or more messages) that may include or otherwise be associated with acomponent, such as the word “men,” which is depicted in the example ofdiagram 100. Performance management platform 160 may include logic toanalyze various levels of performance based on the usage of the term“men” in previous posts. Likewise, performance management platform 160may determine a level of performance for the usage of the term “persons”in past posts or electronic messages. In this example, performancemanagement platform 160 may determine that inclusion of the term“persons” may provide an engagement value of +1.100, whereas the term“men” may provide an engagement value of +0.250. As “persons” may beviewed as a synonym (or as a suitable substitute) for “men,” messagegenerator 162 may (e.g., automatically, in some cases) replace the term“men” with the term “persons” so as to increase a level of engagement bya predicted amount (e.g., the difference between +1.100 and +0.250).

Similarly, performance management platform 160 may be configured toreceive data 174 (e.g., electronic messages, posts, webpages, emails,etc.) from any number of platforms 110 a, 110 b, 113 a, and 113 b todetermine components and corresponding characteristics or attributesthat may be used by entities external to message generation system 150.Performance management platform 160 also may be configured to analyzeand characterize one or more levels of performance for messagecomponents in data 174 (e.g., electronic messages generated by platforms110 a, 110 b, 113 a, and 113 b). Thus, components derived from data 174may be characterized with respect to a performance metric (e.g., a valueof engagement), and may be stored in aggregated message data repository144. Continuing with the example of diagram 100, engagement values of+1.100 and +0.250 (or portions thereof) may be derived based on eitheruser account message data in repository 142 or aggregate message data inrepository 144, or a combination thereof. According to some examples,performance metric criteria and any other data may be stored inperformance data repository 146, including data representing one or moreperformance curves. A performance curve, at least in some non-limitingexamples, may include data representing a performance metric (e.g., anumber of impressions) as a function of time, or any other performancemetric or contextual parameter.

To illustrate operation of performance management platform 160, considerthat performance management platform 160 may receive data signals 170(e.g., from a user interface associated with computing device 120) tocause formation of an electronic message 124. Message generator 162 maybe configured to identify one or more performance metric values, such asone or more engagement values, assigned to one or more portions (orcomponents, such as the word “men”) of electronic message 124. Further,performance metric adjuster 164 may be configured to determine anequivalent to a portion of electronic message 124 to enhance aperformance metric value. Here, performance metric adjuster 164 may beconfigured to determine a word or term “persons” is equivalent (e.g., asa synonym) to “men,” and may be further configured to substitute theequivalent (e.g., equivalent word) in place of a message portion to forman adapted electronic message 172. Thereafter, adapted electronicmessage 172 may be published (e.g., transmitted) in accordance with, forexample, a scheduled point in time. According to various examples,message generator 162 is configured to generate various formattedversions of adapted electronic message 172, whereby each formattedversion may be compatible with a particular platform (e.g., socialnetworking platform). Thus, adaptive electronic message 172 can betransmitted via a network 111 for presentation on user interfaces on aplurality of computing devices 109 a, 109 b, 109 c, and 109 d. Also,message generator 162 may be configured to format various data forgraphically presenting information and content of electronic message 124on a user interface of computing device 120.

According to some examples, performance management platform 160 may befurther configured to implement a performance analyzer 166 and apublishing optimizer 164. Performance analyzer 166 may be configured toperform an analysis on one or more components of a message prior topublishing so as to determine whether one or more components of themessage comply with one or more performance metric criteria. Further,performance analyzer 166 may be configured to identify one or morecomponent characteristics or attributes that may be modified so as toallow an electronic message to comply one or more performance criteria.According to some examples, performance analyzer 166 may be configuredto analyze various amounts of message data from various data sources toidentify patterns (e.g., of microsegments) of message recipients atgranular levels so as to identify individual users or a subpopulation ofusers.

Publishing optimizer 168 may be configured to determine an effectivenessof an electronic message relative to one or more performance metrics andtime. In some examples, publishing optimizer 168 may monitor values of aperformance metric against a performance criterion to determine when aneffectiveness of an electronic message is decreasing or has reached aparticular value. Responsive to determining reduced effectiveness,publishing optimizer 168 may be configured to implement anotherelectronic message.

FIG. 2 depicts another example of an electronic message performancemanagement platform, according to various examples. Diagram 200 depictsa performance management platform 260 including a data collector 230,which, in turn, includes a natural language processor 232 and ananalyzer 234, a message generator 262, a performance metric adjuster264, and a publication transmitter 266. Performance management platform260 may be configured to receive data 201 a, which may includeelectronic message data from a particular user account or from anynumber of other electronic accounts (e.g., social media accounts, emailaccounts, etc.). Further, performance management platform 260 may beconfigured to publish an electronic message 201 c via network 211 to anynumber of message networked computing devices (not shown). In one ormore implementations, elements depicted in diagram 200 of FIG. 2 mayinclude structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

Data collector 230 is configured to detect and parse the variouscomponents of an electronic message, and further is configured toanalyze the characteristics or attributes of each component, as well asto characterize a performance metric of a component (e.g., an amount ofengagement for a component). Natural language processor 232 may beconfigured to parse (e.g., using word stemming, etc.) portions of anelectronic message to identify components, such as a word or a phrase.Also, natural language processor 232 may be configured to derive orcharacterize a message as being directed to a particular topic based on,for example, known sentiment analysis techniques, known content-basedclassification techniques, and the like. In some examples, naturallanguage processor 232 may be configured to apply word embeddingtechniques in which components of an electronic message may berepresented as a vector of numbers. As shown, natural language processor232 includes a synonym generator 236 configured to identify synonyms orany other suitably compatible terms for one or more words in anelectronic message being generated (e.g., prior to publication). Forexample, synonym generator 236 may be configured to identify the term“U.S.A.” as a synonym, or suitable substitution, for the term “America.”In at least one example, synonym generator 236 may be configured tocompare two or more components (e.g., two or more words andcorresponding vectors) to determine a degree to which at least twocomponents may be similar, and, thus may be used as synonyms. A degreeof similarity between two words may be derived by determining, forexample, a cosine similarity between respective vectors of the words.Note that synonym generator 236 may determine substitutable words basedon hierarchical relationships (e.g., substituting the word “China” forthe word “Beijing”), genus-species relationships, or any otherrelationships among similar or compatible words or components.

Analyzer 234 may be configured to characterize various components toidentify characteristics or attributes related to a component, and mayfurther be configured to characterize a level of performance for one ormore performance metrics. Analyzer 234 includes a message componentattribute determinator 235 and a performance metric value characterizer237, according to the example shown. Message component attributedeterminator 235 may be configured to identify characteristics orattributes, such as message attribute data 203, for a word, phrase,topic, etc. In various examples, message attribute data 203 may beappended, linked, tagged, or otherwise associated with a component toenrich data in, for example, user account message data repository 242and aggregate message data repository 244. A synonym may be acharacteristic or an attribute of a message component. Examples ofmessage attribute data 203 are depicted as classification data 203 a(e.g., an attribute specifying whether a component may be classified asone or more of a word, phrase, or topic), media type data 203 b (e.g.,an attribute specifying whether a component may be classified as beingassociated with an email, a post, a webpage, a text message, etc.),channel type data 203 c (e.g., an attribute specifying whether acomponent may be associated with a type of social networking system,such as Twitter). Other metadata 203 d may be associated with, or taggedto, a word or other message component. As such, other metadata 203 d mayinclude a tag representing a language in which the word is used (e.g., atag indicating English, German, Mandarin, etc.). Other metadata 203 dmay include a tag representing a context in which a word is used in oneor more electronic messages, such as in the context of message purpose(e.g., a tag indicating a marketing campaign, or the like), an industryor activity (e.g., a tag indicating an electronic message componentrelating to autonomous vehicle technology, or basketball), etc. In somecases, other metadata 203 d may include data representing computedvalues of one or more performance metrics (e.g., a tag indicating valuesof an amount of engagement, etc.) as characterized by performance metricvalue characterizer 237.

Performance metric value characterizer 237 may be configured to evaluatea components and corresponding characteristics or attributes tocharacterize a value associated with the performance metric. Forexample, a value of engagement as a performance metric may be computedas a number of interactions, including different types of interactions(e.g., different user input signals). Each interaction may relate to aparticular user input, such as forwarding a message (e.g., select a“retweet” input in association with a Twitter social messaging computingsystem), activating a link, specifying a favorable response (e.g.,select a “like” input), and the like. As another example, a value ofengagement may be computed as a number of interactions per unit time,per number of electronic message accesses (e.g., impressions), or anyother parameter. Values of engagement may be determined in any way basedon message interactions. Further, performance metric value characterizer237 may be configured to compute impressions, reach, click-throughs, anumber of times a message is forwarded, etc. According to variousexamples, performance metric value characterizer 237 may be configuredto analyze a corpus of electronic messages stored in repositories 242and 244 to derive one or more of the above-mentioned performance metricsfor each of a subset of words or other components.

Diagram 200 further depicts performance management platform 260including a message generator 262 configured to generate messages, and aperformance metric adjuster 264 configured to adjust or modify a valueof a performance metric by, for example, replacing a component inexchange, for example, with another component (e.g., a synonym) having agreater value for the performance metric. According to some examples,performance data repository 246 may include various sets of performancecriteria with which to guide formation of an electronic message. Forexample, a component of an electronic message being generated may beassociated with a value that is predicted to be noncompliant with atleast one performance criterion (e.g., a certain desired level ofperformance over a period of time). Thus, performance metric adjuster264 may be configured to identify one or more actions that may adapt theelectronic message so as to conform to the performance criteria. Forexample, a subset of performance criteria may be selected to evaluategeneration of electronic message, whereby the subset of performancecriteria may specify that a relatively high engagement value is a goalto attain within a relatively short window of time. In this case, a user(e.g., a marketer) may be interested in a quick spike in engagementfollowed by another electronic message. Thus, a sustainable engagementrate over a longer period of time may not be desired. Consequently,performance metric adjuster 264 may identify, for example, synonyms thathave been characterized as having performance levels that may conform tothe desired performance criteria (i.e., a relatively high engagementvalue to be obtained within a relatively short window of time). Somesynonyms, such as those associated with moderate engagement values thatsustain over longer periods of time, may be excluded for implementationin this example.

Publication transmitter 266 may be configured to generate any number ofplatform-specific electronic messages based on an adapted electronicmessage. Thus, publication transmitter 266 may generate an electronicmessage or content formatted as, for example, a “tweet,” a Facebook™post, a web page update, an email, etc.

FIG. 3 is a diagram of an example of a user interface depicting adaptionof an electronic message during generation, according to someembodiments. Diagram 300 depicts a message generator 362 be configuredto present a message generation interface 302 (e.g., as a userinterface) with which to generate an electronic message 304. In theexample shown, a user having an electronic social media accountidentified as “Kaneolli Racing” is generating electronic message 304with at least text as content. One of the purposes of electronic message304 may include promoting Kaneolli Racing (e.g., offering a racing shirtduring a European bike race). Kaneolli Racing is a purveyor of racingbicycles, as well as other bicycles, such as mountain bikes, BMX bikes,etc. During or after creation of a proposed electronic message 304,performance metric adjuster 364 may be configured to identifycomponents, such as words, that may have equivalent terms (or othersubstitutable terms) that may replace or augment words to predictivelyenhance a performance level of electronic message 304 prior topublishing. In one or more implementations, elements depicted in diagram300 of FIG. 3 may include structures and/or functions as similarly-namedor similarly-numbered elements depicted in other drawings.

In the example shown, message generator 362 generates a graphicrepresentation (“+1.224%”) 312 indicative of a level of performanceassociated with a term (“TourdeFrance”) 310. In this example, the valuesof engagement are depicted as values of a performance metric. Similarly,message generator 362 generates graphic representation (“+0.600%”) 316indicative of a level of performance associated with a word (“Kaneolli”)314, graphic representation (“−0.110%”) 324 indicative of a level ofperformance associated with a word (“race”) 322, graphic representation(“−0.305%”) 328 indicative of a level of performance associated with aword (“shirt”) 326, and graphic representation (“−0.250%”) 320indicative of a level of performance associated with a word (“BMX”) 318.Hence, words 310 and 314 predictively may enhance engagement forelectronic message 304, whereas words 318, 322, and 326 may degrade orimpair engagement of the message. Graphic representations 312, 316, 328,and 320 may be examples of visual indicators, according to someimplementations.

As for predicted low-performing words 318, 322, and 326, diagram 300depicts an arrangement 360 of equivalent terms and correspondingperformance metrics that may be used to replace one or more of words318, 322, and 326. In some examples, arrangement 360 may be a datastructure stored in, for example, a performance data repository 146 ofFIG. 1. Arrangement 360 need not be presented on message generationinterface 302, and an equivalent term and performance metric value maybe presented (not shown) if a user navigations a user input selectordevice 396 over a graphical representation of interest. As shown, cursor396 transits to or near word 318, and, in response, a graphicalrepresentation depicting equivalent term (“mountain”) 365 andcorresponding engagement value (“+0.375%”) 367 may be displayed (notshown). Thus, a user may select to replace the term “BMX” with the term“mountain,” as mountain may be a suitable replacement that is associatedwith a greater engagement value. In some examples, performance metricadjuster 364 may automatically replace term “BMX” with the term“mountain,” and may optionally replace other terms should higherperformance equivalent terms be available.

In some cases, arrangement 360 may be displayed as a portion of messagegeneration interface 302. As shown, lower performing words 318, 322, and326 may be included as terms 361 in respective rows 370, 372, and 374.Engagement values depicted in graphical representations 320, 324, and328 are also shown as including as engagement values 363 in arrangement360. Alternate equivalent terms 365, such as “Tour de France,”“mountain,” and “jersey,” are shown to be associated with respectiveengagement values 367, such as +1.224%, +0.375%, and +0.875%. As theterm “jersey” is associated with a greater engagement value than theterm “shirt,” the term jersey may be substituted to replace the termshirt in electronic message 304 to enhance performance of the messagepredictively.

FIG. 4 is a diagram of an example of a user interface depicting adaptionof an electronic message during generation, according to someembodiments. Diagram 400 depicts a message generator 462 and aperformance metric adjuster 464 configured to access performance metricdata, such as engagement values 410. In the example shown, a number ofterms, some of which may be equivalents, are depicted with acorresponding engagement value 410 and at a number of messages 450 thatinclude the term (e.g., expressed as a percentage, %, of messages with aterm). According to some examples, representation 402 depicts variousgroupings of terms that, while not required, may be presented via a userinterface 401 to a user for identifying candidate equivalent terms andpredictive effects (e.g., values of engagement) of using the equivalentterms. In one or more implementations, elements depicted in diagram 400of FIG. 4 may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

In this example, representation 402 depicts at least categories ofterms, which may be implemented as attributes, based on frequency 450 ofmentioned terms and corresponding engagement rates 410. A first grouping420 of terms includes message components having relatively effective(e.g., higher) engagement values, and have fewest numbers of mentions(e.g., used least in electronic messages). Grouping 420 includes terms“Tour de France” 422 and “jersey” 424. With fewest usages, performancemetric adjuster 464 may be configured to automatically implement theseterms to enhance engagement of electronic messages with these terms. Asecond grouping 430 of terms includes message components havingmoderately effective engagement values, and have moderate numbers ofmentions (e.g., used moderately in electronic messages). Grouping 430includes terms “touring” 432 and “mountain” 434. With moderate usages,performance metric adjuster 464 may be configured to automaticallycontinue to implement these terms to continue sustaining engagement ofelectronic messages with these terms. A third grouping 440 of termsincludes message components having least effective engagement values,and these terms have a range of numbers of mentions in electronicmessages. Grouping 440 includes terms “shirt” 442 and “BMX” 444. In someexamples, performance metric adjuster 464 may be configured toautomatically deemphasize usage of these terms to reduce risks ofencumbering the enhancement of engagement values for the electronicmessages. By analyzing language patterns expressed representation 402,users (e.g., marketers) can test different tactics to monitor responsesof using particular words or message components.

FIG. 5 is a diagram of an example of identifying performance metricsrelative to a geographic location for an electronic message duringgeneration, according to some embodiments. Diagram 500 depicts a messagegenerator 562 and a performance metric adjuster 564 configured toenhance performance of an electronic message based on, for example,performance metric values as a function of geographic location.According to some examples, performance metric values of equivalentterms may vary, too, as a function of geographic location. Toillustrate, consider an example in which user interface 500 depicts agraphical representation 502 of various geographical locations at whicha performance metric, such as engagement, for a message componentvaries. In this example, light shading, such as at geographic locations508 (including Fargo, N. Dak.) may have relatively lower values ofengagement for a term “Tour de France.” In moderately-shaded areas thatinclude geolocations 510, the term “Tour de France” may have arelatively moderate range of engagement values for the term “Tour deFrance,” whereas in darkly-shaded areas that include geographiclocations 512, the term “Tour de France” may have relatively higherengagement values. In one or more implementations, elements depicted indiagram 500 of FIG. 5 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings.

Data arrangement 520 depicts increasing values of engagement 523 forterm 521 from geolocations 525 ranging from Fargo, N. Dak. (e.g., ingeographic regions 508) to Miami, Fla. (e.g., in geographic regions510), and Miami Fla. to Los Angeles Calif. (e.g., in geographic regions512). In some cases, performance metric adjuster 564 may use the term“Tour de France” in row 528 when an electronic message is configured totarget recipients in Los Angeles. However, an equivalent term “race” inrow 529 may yield greater engagement values when used in electronicmessages targeted to recipients in Fargo, N. Dak., rather than using theterm “Tour de France” in row 524. As such, performance metric adjuster564 may be configured to automatically implement the term “race” whenpropagating electronic message to North Dakota rather than using termsand corresponding engagement values in rows 524, 526, and 528.

FIG. 6 is a diagram of an example of identifying a level of complexityof components for selecting a component in an electronic message duringgeneration, according to some embodiments. Diagram 600 depicts a messagegenerator 662 configured to generate a message complexity interface 602,and a performance metric adjuster 664 configured to generate anelectronic message including one or more components having valuesassociated with performance metric values compliant with performancecriteria. For example, performance criteria for an electronic messagemay specify a reading level associated with targeted recipients of themessage. Hence, performance metric adjuster 664 may identify a messagecomponent, such as a term 621 (e.g., “race,” “BMX,” or “shirt”) that maybe less compatible that an alternative term 625 (e.g., “Tour de France,”“Mountain,” or “jersey”). In one or more implementations, elementsdepicted in diagram 600 of FIG. 6 may include structures and/orfunctions as similarly-named or similarly-numbered elements depicted inother drawings.

According to the example shown in data arrangement 620, terms 621 inrows 622, 624, and 626 may have corresponding complexity values 623,such as “5,” “6,” and “7,” whereas alternate terms 625 may havecorresponding complexity values 627 (e.g., “12,” “8,” and “13”). Notethat a complexity level of a message component, such as a word, mayrelate to a reading level based on, for example, the Gunning Fog Index,which is an approach for estimating a number of years of formaleducation. Other techniques for describing a level complexity beyond theGunning Fog Index may be used in various implementations. According tosome examples, logic in an electronic message performance managementplatform may be configured to analyze content of a sample of electronicmessages of a subpopulation of recipients to determine one or morereading levels. The subpopulation of recipients that are most likely tobe responsive to a generated electronic message may be at least onegroup to target. As such, performance metric adjuster 664 may beconfigured to substitute out, for example, the word “Tour de France”having a reading level (or level of complexity) of “12,” whereas atargeted subpopulation of recipients may be described as having areading level of “7.” Thus, the term “race,” which is associated with areading level of “5” may be more appropriate and comprehendible byrecipients associated with a reading level of 7.

In one example, logic in an electronic message performance managementplatform may be configured to characterize a word as a portion of theelectronic message to form a characterized word including acharacteristic. In some examples, a characteristic may include a levelof complexity for a word (e.g., “Tour de France”), the level ofcomplexity being indicative of a reading level. Hence, the logic may beconfigured to identify a reading level associated with a subpopulationof recipient computing devices of an electronic message, and to identifyanother word (e.g., “race”) having a different level of complexity(e.g., a lower level) relative to the level of complexity for the word“Tour de France.” Then, the logic may be configured to embed word “race”into the electronic message to form an adapted electronic message for atargeted subpopulation of recipient computing devices.

FIG. 7 is a diagram of an example of identifying subpopulation-dependentcomponents for an electronic message during generation, according tosome embodiments. Diagram 700 depicts a message generator 762 configuredto generate a subpopulation expansion interface 702 configured to expanda reach of an electronic message by targeting a particular subpopulationof recipients. Diagram 700 also depicts a performance metric adjuster764 configured to adjust a performance metric value by, for example,selecting an equivalent term (e.g., alternative term) to calibrate alevel of performance of a word to a particular subpopulation for whichan electronic message is being generated. In one or moreimplementations, elements depicted in diagram 700 of FIG. 7 may includestructures and/or functions as similarly-named or similarly-numberedelements depicted in other drawings.

In the example shown, data arrangement 720 includes a subset of terms721 corresponding to performance metric values 723 for a first targetedsubpopulation, whereas another subset of alternate terms 725 correspondto performance metric values 725 for a second targeted subpopulation.According to various examples, the two targeted subpopulations maydiffer from each other by demographics, purchasing behaviors, incomes,or any other characteristic. A set of performance criteria may definehow best to generate electronic messages for optimizing engagement basedon the subpopulation. Consequently, performance metric adjuster 764 maybe configured to modify or adapt a word of an electronic message so asto more precisely generate electronic messages that may yield apredictive amount of engagement or other performance metrics. In atleast one case, identifying subpopulation-dependent components mayfacilitate the enhancement of values of a performance metric to increaselevels of engagement.

FIG. 8 is a flow diagram as an example of generating an adaptedelectronic message, according to some embodiments. Flow 800 may be anexample of modifying one or more components of an electronic message toenhance one or more performance metric values. At 802, data signals tocause formation of an electronic message may be received from, forexample, a user interface. In some cases, the data signals are receivedinto an electronic message performance management platform. At 804, oneor more performance metric values, such as engagement values, may beassigned to one or more portions (e.g., one or more words) of anelectronic message. The values of a performance metric may be identifiedat 804. At 806, an equivalent component (e.g., a synonym or any othercompatible term or component) may be determined to enhance (e.g.optimize) a rate of transmission or propagation of an electronicmessage. At 808, an equivalent term may be substituted in place ofinitial term, thereby forming an adapted electronic message. At 810,data may be received to set a scheduled time at which the adaptedelectronic message may be published. For example, a user may schedule apublishing of an adapted electronic message at a scheduled time duringwhich a subset of recipients have demonstrated frequent engagementactivities relative to other time periods.

FIG. 9 is a diagram depicting an example of an electronic messageperformance management platform configured to harvest and analyzeelectronic messages, according to some examples. Diagram 900 includes aperformance management platform 960 including a data collector 930, amessage generator 962, and a performance metric adjuster 964. Further,data collector 930 is shown to include an analyzer 934, which, in turn,includes a component characterizer 972, a performance curve generator974, a performance curve predictor 975, and a performance metriccorrelator 976, any of which may be implemented in hardware or software,or a combination of both. In one or more implementations, elementsdepicted in diagram 900 of FIG. 9 may include structures and/orfunctions as similarly-named or similarly-numbered elements depicted inother drawings.

Analyzer 934 may be configured to data mine and analyze relatively largenumber of datasets with hundreds, thousands, millions, etc. of datapoints having multiple dimensions and attributes. Further, analyzer 934may be configured to correlate one or more attributes to one or moreperformance metric values so that implementation of a component of anelectronic message may be predicted to cause a predicted level ofperformance, according to some examples. For example, analyzer 934 maybe configured to identify a subset of terms that may be used, assynonyms, to replace a word to predictably increase or enhance aperformance metric value of a word as well as an electronic messageincluding the word.

Component characterizer 972 may be configured to receive data 907representing a proposed electronic message and data 901 a representingelectronic messages and any other selected source of data from whichcomponents (e.g., words, phrases, topics, etc.) of one or more subsetsof electronic messages (e.g., published messages) may be extracted andcharacterized. In some examples, component characterizer 972 may beconfigured to identify attributes with that may be characterized todetermine values, qualities, or characteristics of an attribute. Forinstance, component characterizer 972 may determine attributes orcharacteristic that may include a word, a phrase, a topic, or anymessage attribute, which can describe the component. A message attributemay include metadata that describes, for example, a language associatedwith the word (e.g., a word is in Spanish), or any other descriptor,such as a synonym, a language, a reading level (e.g., a level ofcomplexity), a geographic location, and the like. Message attributes mayalso include values of one or more performance metrics (e.g., one ormore values of engagement, impressions, etc.). In some examples,component characterizer 972 may implement at least structural and/orfunctional portions of a message component attribute determinator 235 ofFIG. 2.

Performance curve generator 974 may be configured to statisticallyanalyze components and attributes of electronic messages to identifypredictive relationships between, for example, an attribute and apredictive performance metric value. In this example, a subset ofpredictive performance metric values associated with one or moreattributes may be described as a “performance curve.” According to someexamples, a performance curve may include data representing a value of aperformance metric as a function of time (or any other metric orparameter). For example, a performance curve associated with one or moreattributes may specify an amount of engagement (e.g., an engagementvalue) as a function of time (e.g., a point in time after an electronicmessage is published). According to some embodiments, performance curvegenerator 974 may be configured to classify and/or quantify variousattributes by, for example, applying machine learning or deep learningtechniques, or the like. In one example, performance curve generator 974may be configure to segregate, separate, or distinguish a number of datapoints representing similar (or statistically similar) attributes,thereby forming one or more clusters 921 of data (e.g., in 3-4 groupingsof data). Clustered data 921 may be grouped or clustered about aparticular attribute of the data, such as a source of data (e.g., achannel of data), a type of language, a degree of similarity withsynonyms or other words, etc., or any other attribute, characteristic,parameter or the like. While any number of techniques may beimplemented, performance curve generator 974 may apply “k-meansclustering,” or any other known clustering data identificationtechniques. In some examples, performance curve generator 974 may beconfigured to detect patterns or classifications among datasets andother data through the use of Bayesian networks, clustering analysis, aswell as other known machine learning techniques or deep-learningtechniques (e.g., including any known artificial intelligencetechniques, or any of k-NN algorithms, regression, Bayesian inferencesand the like, including classification algorithms, such as Naive Bayesclassifiers, or any other statistical or empirical technique).

Performance curve generator 974 also may be configured to correlateattributes associated with a cluster in clustered data 921 to one ormore performance curves 923 based on, for example, data in message datarepository 941 that may represent any number of sample sets of data fromelectronic messages. According to some embodiments, a “performancecurve” may represent performance of one or more message components(e.g., one or more words or terms), or attributes thereof, such that amessage component, if used, may influence or otherwise contribute toenhancing a value of a performance metric, such as an engagement rate.For example, a term “Tour de France” may be determined to generate acertain engagement value per unit time. In some examples, a performancecurve 923 a for the term “Tour de France” may represent an influence ofthe term as a function of time, t. Here, a value of engagement (whetherdetermined empirically or predictively) may vary relative to time, t, inwhich a level of engagement may reach a value “A” during time “t” suchthat, cumulatively, the term “Tour de France” may have a totalcumulative engagement of “X” (e.g., an area under the curve shown). Inanother example, the term “Tour de France,” or its synonym, may giverise to a performance curve 923 b. In this case, a level of engagementmay reach a value “B” during and after time “t” such that, cumulatively,the term “Tour de France” may have a total cumulative engagement of “Y,”which may provide a maximal, sustainable engagement rate over a longerperiod of time (e.g., slowly increasing to time “t” and maintaining avalue “B” over time). Alternatively, in yet another example, the term“Tour de France” may provide for a performance curve 923 c in which alevel of engagement may quickly reach a value “C,” which is greater thanvalues “A” and “B” during after time “t.” Thus, while performance curve923 c may indicate a performance metric quickly can reach a large valueof engagement, subsequent values of performance curve 923 c indicate arelatively steep reduction in engagements, with less cumulative totalengagements (e.g., Z) than performance curves 923 a (e.g., X) and 923 b(e.g., Y). Performance curves 923 a, 923 b, and 923 c are non-limitingexamples in which one or more message components may be used to predictfuture performance of a published electronic message. In some cases, amarketer may select a performance curve 923 with which to publish anelectronic message.

Further, performance management platform 960 may be configured togenerate any number of performance curves 923 associated with any of oneor more message components. Consequently, a user 908 may generate aproposed electronic message at user computing device 909, which, inturn, may provide an electronic message and its components toperformance management platform 960 for analysis. In some cases, anapplication associated with computing device 909 may specify, in a userinterface 918, that a predicted performance metric value for aparticular component or message may not meet particular performancecriteria. As such, user 908 may provide a user input with user interface918 to enhance one or more performance metrics, as set forth in data907. In some examples, one or more performance curves 923 may begenerated based on, for example, cluster analysis, curve matching, orany other known analytical techniques to characterize clustered data,according to some embodiments.

In accordance with various examples, a user 908 may wish to generate anelectronic message for publication that is designed to meet certainvalues of performance metrics and the like. In the example shown,performance curve predictor 975 may be configured to receive data 907,which may include contents (e.g., components, such as text, video,audio, etc.) of a proposed electronic message. During, or subsequent to,a message generation process, performance curve predictor 975 may beconfigured to generate a predicted performance curve 925 based on theproposed electronic message and its components, such as electronicmessage 304 of FIG. 3, to determine one or more performance metricvalues associated with a newly-generated electronic message. In at leastone example, performance curve 925 may be compared against otherperformance curves 923 to determine a correlation between a proposedelectronic message 907 and an archived corpus of messages. As such, acurve matcher module 999 may be configured to match predictedperformance curve 925 against performance curves 923 a, 923 b, and 923 cto identify one or more sets of message components that may beassociated with performance curve 925.

In one embodiment, a specific performance curve 923 may be relativelyclose to predicted performance curve 925. Curve matcher 999 may beconfigured to determine which of performance curves 923 a to 923 c maybe most relevant to an electronic message 907. In some cases, curvematcher 999 is configured to perform curve matching or curve fittingalgorithms to identify associated attributes. For example, if curvematcher 999 identifies performance curve 923 b as most relevant, thencurve matcher 999 may be configured to identify message componentscontributing to performance curve 923 b so that a pending message may beadapted to use those message components. As such, an electronic messageincorporating adapted components may be used to transmit or convey amessage at a rate of transmission or propagation, as described herein.

Message generator 962 may be configured to generate a message based onuser input, as well as information provided by performance metriccorrelator 976, which may be configured to identify subsets of messagecomponents (e.g., words, topics, etc.) for generating an electronicmessage that comports to one or more performance criteria. Performancemetric adjuster 964 is configured to adapt one or more components orwords of an electronic message by adjusting performance metric for anelectronic message by modifying or a placing a particular term.Thereafter, an electronic message may be formatted in transmitted asdata 901 c via networks 911 to any number of social media networkcomputing devices.

FIG. 10 is a diagram depicting an example of a user interface configuredto accept data signals to identify and modify predicted performance of amessage component, according to some examples. Diagram 1000 includes auser interface 1002 configured to depict or present data representing“predictive performance” 1004, data representing “geographic location”1006, and data representing “terms” (as message components) set forth intab 1008. As shown, interface 1002 depicts a graphical representation1020 of various performance metric values, a function of time, for oneor more terms. As shown, a term “Tour de France” 1032 is shown to havevariable values of a performance level metric, such as engagement 1022,relative to time. Further, a term “jersey” 1034, a term “mountain” 1036,and a term “shirt” 1038 are also depicted as having variable magnitudesof a performance over a period of time, until time point at 1009. Insome cases, time point at 1009 may refer to a present point in time,according to some examples, at which a user or computing device ismonitoring performance of a published electronic message.

According to some examples, user interface 1002 may be configured topresent predicted performance values 1030 over a number of messagecomponents or words. Further to diagram 1000, predicted performancevalues 1030 may include predicted values 1033 of the term “Tour deFrance,” predicted values 1035 of the term “jersey” 1034, predictedvalues 1037 of the term “mountain” 1036, and predicted values 1039 ofthe term “shirt” 1038. Therefore, user interface 1002 may be configuredto present graphical representations of predicted performance values1030 in a user interface. Should one of predicted performance values1030 be determined to be less desired, a user may modify a term of theelectronic message to ensure performance criteria are met.

Also, a user may monitor performance of one or more of messagecomponents in real-time (or near real-time) to determine whether anelectronic message, such as a post to a website, is performing asexpected (e.g., in accordance with one or more performance metriccriteria). As shown, user may select an engagement value 1099 at a timepoint, T, via user input selector 1098 to identify the performance ofthe term “Tour de France” at time point T. In some examples, dataarrangement 1060 may be displayed responsive to selecting time point T,whereby data arrangement 1060 may present various performance metrics ata particular point in time. Data arrangement 1060 may be presented toconvey that a particular term 1061 may be associated with performancemetrics 1063, 1065, 1067, or 1069. For example, each term in respectiverows 1062, 1064, 1066, and 1068 may be associated with an engagementmetric 1063, a number of messages 1065, a peak number of messages 1067,and a number of messages transmitted (or interacted with) per minute(“MPM”) 1069.

FIG. 11 is a diagram depicting an example of a user interface configuredto accept data signals to visually convey a predicted performance of amessage component, according to some examples. Diagram 1100 includes auser interface 1102 configured to depict or present data representing“predictive performance” 1104, data representing “geographic location”1106, and data representing “terms” (as message components) set forth intab 1108.

As shown, interface 1102 depicts a graphical representation 1120 ofvarious performance metric values and visually-identifiable magnitudesof the values of a performance metric, such as an engagement rate. Asshown, term “Tour de France” 1125, “jersey” 1130, “mountain 1140,” and“shirt” 1145 may be presented as synonyms or related terms to a topic“bike racing” (e.g., for purposes of substituting one or more terms foreach other to enhance performance). In diagram 1100, term “Tour deFrance” 1125 is shown to have a relatively large circular size comparedto the other terms. Therefore, in this case, the term “Tour de France”may have a relatively larger engagement value than the other termspresented. Each term 1125, 1130, 1140, and 1145 may be presentedencapsulating smaller visual indicators 1121 (e.g., circles) that conveya subset of synonyms for each term.

Interface 1102 may also include a user input field 1110 to accept userinput (e.g., a new term) to search, discover, and modify presentation ofgraphical representation 1120 by adding a visual indicator 1112 of a newterm to “bike racing.” In some cases, sizes of the visual indicators(e.g., circles) for terms 1125, 1130, 1140, and 1145 may be adjusted insize to accommodate the visual indicator 1112 of the new term. Further,interface 1102 may present data arrangement 1160 to convey that aparticular term 1161 may be associated with performance metrics 1163,1165, 1167, or 1169. For example, each term in respective rows 1162,1164, 1166, and 1168 may be associated with an engagement metric 1163, anumber of messages 1165, a peak number of messages 1167, and a number ofmessages transmitted (or interacted with) per minute (“MPM”) 1169. Row1170 may be generated to display corresponding performance metric valuesas new term 1112 is added to “bike racing.”

FIG. 12 is a flow diagram as an example of predicting performancemetrics for an electronic message, according to some embodiments. Flow1200 may begin at 1202, whereby data signals from a user interface maybe received, for example, to initiate formation of an electronicmessage. In at least some examples, flow 1200 may be configured to guidegeneration of an electronic message based on whether predicted componentattribute values comply with performance metric criteria. Performancemetric criteria, for example, may include a threshold or a range ofperformance metric values that may be designed to test whether values ofa monitored component characteristic or performance metric comply with adefined set of values (e.g., performance criteria). To illustrate,consider that a user may craft an electronic message for publication,whereby the user is concerned with longevity, or sustainability, of anelectronic message to achieve a certain level of performance (e.g., avalue of an amount of engagement) as a function of time. Thus, the usermay be interested in generating electronic messages with componentspredicted to solicit sustainable amounts of engagement, rather than, forexample, configuring an electronic message and contents to cause arelatively sharp rate, or spike, such that the amounts of engagementprovide a rapid response. An example of the latter may be a massive“push” campaign designed to extend a reach over greater number ofrecipients in a relatively short duration (e.g., at high amounts ofengagement) regardless of whether such performance levels areunsustainable for anything other than least a short duration of time.

At 1204, a component, such as a word, topic, or any other attribute, ofan electronic message may be determined prior to publication. Accordingto some examples, a component and/or its attributes may be characterizedto identify a type or quantity (or value) associated with the componentor attribute.

At 1206, one or more performance criteria for an electronic message maybe identified, whereby a performance criterion may define whetherformation of an electronic message is compliant with a value of theperformance criterion. In some cases, a performance criterion mayinclude data representing a value as a function of time. For example, arate of engagement may increase during a first time period, and then maymaintain a value within a range of engagement rate values during asecond time period. At a third time period, a performance criterion maybe used to determine whether the rate of engagement for an electronicmessage component is out of range or non-compliant. If non-compliant, adetermination may be made whether to deactivate use or publication of anelectronic message in favor of another electronic message. According tosome embodiments, a set of values for a performance criterion orcriteria may define a “performance curve,” by which, for example, apredicted engagement value per unit time may comport with the curve. Insome examples, identifying message performance criteria may includeidentifying a performance curve associated with at least one performancemetric.

At 1208, a message component may be characterized to identify acomponent attribute, which may have a value that may be measured againsta message performance criterion to identify a component attribute. At1210, a value of a component attribute may be predicted to match atleast one of the message performance criteria. In some examples, a valueof a component characteristic may be predicted as a value of a“performance curve” in which a value of a performance metric, such asengagement, may vary as a function of time. Therefore, during generationof an electronic message, a performance management platform may beconfigured to characterize a component at 1206 and determine (e.g.,predict) whether the component (or an attribute thereof) is associatedwith a performance metric value at 1208 that comports with a performancecriterion. For example, if a component, such as a term “pizza” isassociated with a particular engagement value based on “New York” as angeographic-related attribute, then logic in the performance managementplatform may compute whether an engagement value associated with theterm “pizza” comports with an objective to publish an electronic messageadvertising “take-out food” in, for example, “Florida” in accordancewith performance criteria.

Further to this example, a predicted value of engagement that may beanalyzed after an electronic message is published to determine whetherit comports with message performance criteria. For example, a monitoredor computed component characteristic of +0.015% may be compared againsta predicted engagement value of +0.750% over a duration of time “T,”which is less than +0.750%. Thus, in this case, the predicted value ofengagement (i.e., the characterized value of a component “pizza”) may bedetermined to be non-compliant. In some examples, when a predicted valueof a component characteristic (e.g., expressed as a performance metric)of an electronic message is predicted to be non-compliant, a performancemanagement platform may be configured to activate one or more otheractions. For example, a data repository may be accessed to identify analternate component for the electronic message. An example of analternate component is synonym. However, an alternate component and itsattributes may be any type of parameter or attribute with which toselect another component to enhance a predicted performance level of anelectronic message. For instance, an alternate component attributeassociated with an alternate component (e.g., another word or synonym)may be matched against message performance criteria to determine whetherthe use of the alternate component may be predicted to comply withmessage performance criteria. In some embodiments, curve matching orfitting techniques may be used to determine whether an alternatecomponent attribute may match (i.e., comport) with a message performancecriterion. At 1212, an electronic message may be transmitted via anetwork for presentation on a variety of user interfaces at any numberof computing devices.

FIG. 13 is a diagram depicting an electronic message performancemanagement platform implementing a publishing optimizer, according tosome embodiments. Publishing optimizer 1368 may be configured todetermine an effectiveness of an electronic message relative to one ormore performance metrics and time. In some examples, publishingoptimizer 1368 may monitor values of a performance metric against aperformance criterion to determine when an effectiveness of anelectronic message is decreasing or has reached a particular value.Responsive to determining reduced effectiveness, publishing optimizer1368 may be configured to implement another electronic message, as onecorrective action, or any other corrective action to ensure, forexample, a particular set of content may sustainably propagate (e.g.,through any number of multiple forwarding events, such as “retweets” or“shares” at desired rates of transmission and interactivity (e.g.,engagement).

According to some examples, the electronic message performancemanagement platform 1360 of FIG. 13 may be configured to monitor inreal-time (or nearly in real-time) any number of performance metricvalues specifying whether a published electronic message is performingas predicted or otherwise expected. In some cases, a performance metricvalue, such as engagement rate, may be monitored with respect to aperformance curve 1323, such as performance curves 1323 a, 1323 b, or1323 c. When a particular value of the performance metric is detected, adetermination may be made as to whether an associated electronic messagemay be performing suboptimally (e.g., over time relative to aperformance criterion) and whether a corrective action may beimplemented (e.g., modifying the first published electronic message,publishing a second electronic message, etc.).

Diagram 1300 depicts one or more values of a performance metric 1301 andone or more points in time 1303 that may constitute performance criteriawith which to judge or otherwise determine whether performance of apublished electronic message may be complying with the performancecriteria. If not, corrective action may be taken. During time interval1330, a first performance criterion specifies that a value of engagementmay be monitored against a desired engagement value, V2, 1322. Hence, ifmonitored performance metric 1310 fails to comply with desiredengagement value, V2, 1322 during time interval 1330, then correctiveaction may be taken. A second performance criterion may specify a timeinterval 1332 during which a value of engagement is desired to sustain avalue in a range between value (“V2”) 1322 and value (“V3”) 1320. Hence,if the valued of monitored performance metric 1310 is below this range,than the monitor performance metrics 1310 may be deemed noncompliant. Athird performance criterion may specify a value (“V1”) 1324 at whichmonitored performance metric 1310 is deemed minimally effective orineffective. So, if monitored performance metric 1310 is detected tohave a value (“V1”) 1324 at time 1334, then the published electronicmessage may be deemed suboptimal. Corrective action may be taken.According to some embodiments, value (“V1”) 1324 at time 1334 may bedescribed as a “half-life” value (e.g., duration 1334 in which an amountof time elapses such that an electronic message and its contents, suchas a brand promotion, has a value that reaches one-half of an averagevalue of engagement). The above-described performance criteria areexamples and are not intended to be limiting. Thus, monitor performancemetric 1310 may be monitored or compared against any performance ortime-related criteria.

FIG. 14 is a flow diagram as an example of monitoring whetherperformance of an electronic message complies with predicted performancecriteria, according to some embodiments. Flow 1400 may begin at 1402,whereby an electronic message may be published via one or more channels(e.g., various social networking platforms). The electronic message mayinclude data representing a subset of components of electronic message.At 1404, a performance criterion is identified against which aperformance metric associated with the published electronic message maybe monitored. A performance criterion may include one or more time-basedcriteria values during which to, for example, deactivate the firstelectronic message or activate a second electronic message (e.g., atime-related criterion triggers corrective action). A performancecriterion may include one or more performance-based criteria valuesduring which to activate the second electronic message (e.g., aperformance-related criterion triggers corrective action).

At 1406, a value of a performance metric, such a number of impressions,may be monitored. At 1408, a match between one or more values of theperformance metric and the performance criterion may be determined,thereby identifying, for example, a point in time or a value of aperformance metric associated with a published electronic message thatis noncompliant with performance criteria. Hence, a determination may bemade to take corrective action, as well as a type of corrective action.

At 1410, another electronic message may be published via one or morechannels. In some cases, this electronic message may be a new message ormay be based on an earlier message with one or more modified components.A monitored point of time may be matched to one of the one or moretime-based criteria values to initiate activation of a second electronicmessage. Also, a monitored performance metric value may be determined tomatch one or more performance-based criteria values, which may bedefined as triggers to activate publishing of a second electronicmessage.

FIG. 15 is a diagram depicting an electronic message performancemanagement platform implementing a publishing optimizer configured topresent monitored performance values of a published electronic message,according to some embodiments. Diagram 1500 includes an electronicmessage performance management platform 1560 that includes a publishingoptimizer 1568, which may be present a performance metric interface1502. As shown, performance metric interface 1502 may present monitoredperformance metrics, such as message volume 1510 during one or morewindows of time 1512. In at least some cases, a user may implement auser input selector 1598 to cause publishing optimizer 1568 to present amore granular view of performance metrics 1550 during window of time1512. As shown, performance metric interface 1502 may present values andvisual indicators for a number of followers 1551, a number ofimpressions 1552, an amount of engagement 1553, a number of URL clicks1554, a number of conversions 1555, a number of pages reached 1556, andthe like. Performance metric interface 1502 may be viewed ascomputerized tool with which to monitor performance levels of publishedelectronic messages and content to determine whether the messages andcontent are performing as expected to relative to performance criteria.In some examples, performance management platform 1560 may be configuredto automatically perform corrective actions to calibrate content of oneor more electronic messages to one or more sets of performance criteria.

FIG. 16 illustrates examples of various computing platforms configuredto provide various functionalities to components of an electronicmessage performance management platform 1600, which may be used toimplement computer programs, applications, methods, processes,algorithms, or other software, as well as any hardware implementationthereof, to perform the above-described techniques.

In some cases, computing platform 1600 or any portion (e.g., anystructural or functional portion) can be disposed in any device, such asa computing device 1690 a, mobile computing device 1690 b, and/or aprocessing circuit in association with initiating any of thefunctionalities described herein, via user interfaces and user interfaceelements, according to various examples.

Computing platform 1600 includes a bus 1602 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 1604, system memory 1606 (e.g., RAM,etc.), storage device 1608 (e.g., ROM, etc.), an in-memory cache (whichmay be implemented in RAM 1606 or other portions of computing platform1600), a communication interface 1613 (e.g., an Ethernet or wirelesscontroller, a Bluetooth controller, NFC logic, etc.) to facilitatecommunications via a port on communication link 1621 to communicate, forexample, with a computing device, including mobile computing and/orcommunication devices with processors, including database devices (e.g.,storage devices configured to store atomized datasets, including, butnot limited to triplestores, etc.). Processor 1604 can be implemented asone or more graphics processing units (“GPUs”), as one or more centralprocessing units (“CPUs”), such as those manufactured by Intel®Corporation, or as one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 1600exchanges data representing inputs and outputs via input-and-outputdevices 1601, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text driven devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

Note that in some examples, input-and-output devices 1601 may beimplemented as, or otherwise substituted with, a user interface in acomputing device associated with, for example, a user account identifierin accordance with the various examples described herein.

According to some examples, computing platform 1600 performs specificoperations by processor 1604 executing one or more sequences of one ormore instructions stored in system memory 1606, and computing platform1600 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory1606 from another computer readable medium, such as storage device 1608.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 1604 for execution. Such a medium may takemany forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media includes, for example, optical ormagnetic disks and the like. Volatile media includes dynamic memory,such as system memory 1606.

Known forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can access data. Instructions may further betransmitted or received using a transmission medium. The term“transmission medium” may include any tangible or intangible medium thatis capable of storing, encoding or carrying instructions for executionby the machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Transmission media includes coaxial cables, copper wire,and fiber optics, including wires that comprise bus 1602 fortransmitting a computer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 1600. According to some examples,computing platform 1600 can be coupled by communication link 1621 (e.g.,a wired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform1600 may transmit and receive messages, data, and instructions,including program code (e.g., application code) through communicationlink 1621 and communication interface 1613. Received program code may beexecuted by processor 1604 as it is received, and/or stored in memory1606 or other non-volatile storage for later execution.

In the example shown, system memory 1606 can include various modulesthat include executable instructions to implement functionalitiesdescribed herein. System memory 1606 may include an operating system(“O/S”) 1632, as well as an application 1636 and/or logic module(s)1659. In the example shown in FIG. 16, system memory 1606 may includeany number of modules 1659, any of which, or one or more portions ofwhich, can be configured to facilitate any one or more components of acomputing system (e.g., a client computing system, a server computingsystem, etc.) by implementing one or more functions described herein.

The structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or acombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated with one ormore other structures or elements. Alternatively, the elements and theirfunctionality may be subdivided into constituent sub-elements, if any.As software, the above-described techniques may be implemented usingvarious types of programming or formatting languages, frameworks,syntax, applications, protocols, objects, or techniques. As hardwareand/or firmware, the above-described techniques may be implemented usingvarious types of programming or integrated circuit design languages,including hardware description languages, such as any register transferlanguage (“RTL”) configured to design field-programmable gate arrays(“FPGAs”), application-specific integrated circuits (“ASICs”), or anyother type of integrated circuit. According to some embodiments, theterm “module” can refer, for example, to an algorithm or a portionthereof, and/or logic implemented in either hardware circuitry orsoftware, or a combination thereof. These can be varied and are notlimited to the examples or descriptions provided.

In some embodiments, modules 1659 of FIG. 16, or one or more of theircomponents, or any process or device described herein, can be incommunication (e.g., wired or wirelessly) with a mobile device, such asa mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 1659 or one or more ofits/their components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, modules 1659 or one or more of its/their components, or anyprocess or device described herein, can be implemented in one or morecomputing devices (i.e., any mobile computing device, such as a wearabledevice, such as a hat or headband, or mobile phone, whether worn orcarried) that include one or more processors configured to execute oneor more algorithms in memory. Thus, at least some of the elements in theabove-described figures can represent one or more algorithms. Or, atleast one of the elements can represent a portion of logic including aportion of hardware configured to provide constituent structures and/orfunctionalities. These can be varied and are not limited to the examplesor descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit. For example, modules 1659 or one or more ofits/their components, or any process or device described herein, can beimplemented in one or more computing devices that include one or morecircuits. Thus, at least one of the elements in the above-describedfigures can represent one or more components of hardware. Or, at leastone of the elements can represent a portion of logic including a portionof a circuit configured to provide constituent structures and/orfunctionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

1. A method, comprising: receiving data signals to cause formation of an electronic message; determining a component of the electronic message; identifying one or more message performance criteria with which to form the electronic message; characterizing the component to identify a component attribute; predicting the component attribute matches at least one of the message performance criteria configured to vary over a period of time, the component attribute being used to determine engagement with the electronic message by one or more recipients; modifying the electronic message to enhance a rate of propagation at which the electronic message is conveyed as a function of the message performance criteria; transmitting the electronic message via a network for presentation on user interfaces on a plurality of computing devices; monitoring performance of one or more of message components of the electronic message; determining an effectiveness of the electronic message is at an effectiveness value; and invoking an action in a marketing campaign in response to the effectiveness value.
 2. The method of claim 1 wherein the component of the electronic message comprises one or more of a word, a topic, or a message attribute.
 3. The method of claim 1, wherein the message attribute comprises a channel type.
 4. The method of claim 1, wherein the message attribute comprises a topic.
 5. The method of claim 1, wherein the message attribute comprises a word.
 6. The method of claim 1, wherein the message attribute comprises a phrase
 7. The method of claim 1, wherein the message attribute comprises a synonym.
 8. The method of claim 1, wherein the message attribute comprises a language
 9. The method of claim 1, wherein the message attribute comprises a reading level, wherein the reading level is associated with a complexity value representative of a complexity level of the component.
 10. The method of claim 1, wherein the message attribute comprises a geographic location.
 11. The method of claim 1, wherein the message attribute comprises metadata.
 12. The method of claim 1, wherein one or more of the data signals are analyzed by a component characterizer to determine whether the message attribute includes metadata, the metadata being used to determine an amount of engagement.
 13. The method of claim 1, wherein a component characterizer is configured to evaluate metadata associated with the component attribute to determine an amount of engagement, the amount of engagement being assessed over a period of time.
 14. The method of claim 1, wherein a component characterizer is configured to replace the component of the electronic message with another component if an amount of engagement is below a computed value of engagement.
 15. A system, comprising: a memory configured to store data associated with an electronic message and one or more data signals used to construct the electronic message; and a platform configured to receive data signals to cause formation of an electronic message, to determine a component of the electronic message, to identify one or more message performance criteria with which to form the electronic message, to characterize the component to identify a component attribute, to predict the component attribute matches at least one of the message performance criteria configured to vary over a period of time, the component attribute being used to determine engagement with the electronic message by one or more recipients, to modify the electronic message to enhance a rate of propagation at which the electronic message is conveyed as a function of the message performance criteria, to transmit the electronic message via a network for presentation on user interfaces on a plurality of computing devices, to monitor performance of one or more of message components of the electronic message, to determine an effectiveness of the electronic message is at an effectiveness value, and to invoke an action in a marketing campaign in response to the effectiveness value.
 16. The system of claim 15, wherein a performance metric value characterizer is configured to identify one or more message performance curves associated with at least one performance metric to generate an engagement value per unit time as the effectiveness value.
 17. The system of claim 15, further comprising a data repository configured to store the data, the data being used by the platform to identify an alternate component for the electronic message, the alternate component being configured to comport with the message performance criteria.
 18. The system of claim 15, wherein the alternate component is determined by evaluating metadata associated with the component.
 19. The system of claim 15, further comprising: generating a graphical representation of one or more predicted values of the component attribute for the electronic message; and presenting at least one of the one or more predicted values of the component attribute as one or more performance metric values graphically represented in a user interface.
 20. A non-transitory computer readable medium having one or more computer program instructions configured to perform a method, the method comprising: receiving data signals to cause formation of an electronic message; determining a component of the electronic message; identifying one or more message performance criteria with which to form the electronic message; characterizing the component to identify a component attribute; predicting the component attribute matches at least one of the message performance criteria configured to vary over a period of time, the component attribute being used to determine engagement with the electronic message by one or more recipients; modifying the electronic message to enhance a rate of propagation at which the electronic message is conveyed as a function of the message performance criteria; transmitting the electronic message via a network for presentation on user interfaces on a plurality of computing devices; monitoring performance of one or more of message components of the electronic message; determining an effectiveness of the electronic message is at an effectiveness value; and invoking an action in a marketing campaign in response to the effectiveness value. 